Identification of key molecules in COVID-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data.

Background: Although several key molecules have been identified to modulate SARS-CoV-2 invasion of human host cells, the molecules correlated with outcomes in COVID-19 caused by SARS-CoV-2 infection remain insufficiently explored. Methods: This study analyzed three RNA-Seq gene expression profiling datasets for COVID-19 and identified differentially expressed genes (DEGs) between COVID-19 patients and normal people, commonly in the three datasets. Furthermore, this study explored the correlation between the expression of these genes and clinical features in COVID-19 patients. Results: This analysis identified 13 genes significantly upregulated in COVID-19 patients' leukocyte and SARS-CoV-2-infected nasopharyngeal tissue compared to normal tissue. These genes included OAS1, OAS2, OAS3, OASL, HERC6, SERPING1, IFI6, IFI44, IFI44L, CMPK2, RSAD2, EPSTI1, and CXCL10, all of which are involved in antiviral immune regulation. We found that these genes' downregulation was associated with worse clinical outcomes in COVID-19 patients, such as intensive care unit (ICU) admission, mechanical ventilatory support (MVS) requirement, elevated D-dimer levels, and increased viral loads. Furthermore, this analysis identified two COVID-19 clusters based on the expression profiles of the 13 genes, termed COV-C1 and COV-C2. Compared with COV-C1, COV-C2 more highly expressed the 13 genes, had stronger antiviral immune responses, were younger, and displayed more favorable clinical outcomes. Conclusions: A strong antiviral immune response is essential in reducing severity of COVID-19.

Admission SpO2 and ROX index predict outcome in patients with COVID-19.

BACKGROUND: This study aimed to evaluate the accuracy of pulse oximetry-derived oxygen saturation (SpO2) on room air, determined at hospital admission, as a predictor for the need for mechanical ventilatory support in patients with Coronavirus Disease-2019 (COVID-19). METHODS: In this retrospective observational study, demographic and clinical details of the patients were obtained during ICU admission. SpO2 and respiratory rate (RR) on room air were determined within the first 6 h of hospital admission. As all measurements were obtained on room air, we calculated the simplified respiratory rate­oxygenation (ROX) index by dividing the SpO2 by the RR. Based on the use of any assistance of mechanical ventilator (invasive or noninvasive), patients were divided into mechanical ventilation (MV) group and oxygen therapy group. The accuracy of the SpO2, CT score, and ROX index to predict the need to MV were determined using the Area under receiver operating curve (AUC). RESULTS: We included 72 critically ill patients who tested COVID-19-positive. SpO2 on the room air could predict any MV requirement (AUC [95% confidence interval]: 0.9 [0.8-0.96], sensitivity: 70%, specificity 100%, cut-off value ≤78%, P < 0.001). Within the MV group, the use of noninvasive ventilation (NIV) was successful in 37 (74%) patients, whereas 13 patients (26%) required endotracheal intubation. The cut-off ROX value for predicting early NIV failure was ≤1.4, with a sensitivity of 85%, a specificity of 86%, and an AUC of 0.86 (95% confidence interval of 0.73-0.94, P < 0.0001). CONCLUSIONS: A baseline SpO2 ≤78% is an excellent predictor of MV requirement with a positive predictive value of 100%. Moreover, the ROX index measured within the first 6 h of hospital admission is a good indicator of early NIV failure.

I-TRACH: Validating A Tool for Predicting Prolonged Mechanical Ventilation.

PURPOSE: We previously developed a bedside model (I-TRACH), which used commonly obtained data at the time of intubation to predict the duration of mechanical ventilation (MV). We now sought to validate this in a prospective trial. METHODS: A prospective, observational study of 225 consecutive adult medical intensive care unit patients requiring MV. Utilizing the original 6 variables used in the I-TRACH model (Intubation in the ICU, Tachycardia [heart rate > 110], Renal dysfunction [blood urea nitrogen > 25], Acidemia [pH < 7.25], Creatinine [>2.0 or >50% increase from baseline values], and decreased HCO3 [<20]), we (1) confirmed that these were still predictive of length of MV by multivariate analysis and (2) assessed the correlation between the number of criteria met and the subsequent duration of MV. In addition, we compared the performance of I-TRACH to Acute Physiology Age Chronic Health Evaluation-II and III, Sequential Organ Failure Assessment, and Acute Physiology Score as predictors of length of MV. RESULTS: Mean age was 62.6 ± 18.7 years, with a mean duration of MV of 5.8 ± 5.7 days. The number of I-TRACH criteria met directly correlated with the duration of MV. Individuals with ≥4 criteria were significantly more likely to require MV >7 and >14 days. Similarly, those who remained on ventilators for both >7 and >14 days met significantly more I-TRACH criteria than those requiring shorter durations of MV (1.7 ± 1.3 vs 2.8 ± 1.3 vs 3.8 ± 1.3 criteria, P < .001). I-TRACH performed better than all other models used to predict the duration of MV. CONCLUSION: Similar to our previous retrospective study, these findings validate I-TRACH in determining the subsequent need for MV >7 and >14 days at the time of intubation.

Clinical model for predicting prolonged mechanical ventilation.

INTRODUCTION: Mechanical ventilation (MV) predisposes patients to numerous complications, which increases with longer durations of treatment. Identifying individuals more likely to require prolonged MV (PMV) may alter ventilation strategies or potentially minimize the duration of therapy and its associated complications. Our aim was to identify clinical variables at the time of intubation that could identify individuals who will require PMV. METHODS: One hundred thirty consecutive adult patients requiring MV support in a medical intensive care unit (ICU)were retrospectively assessed. Prolonged MV was defined as MV support more than 14 days. RESULTS: Mean age was 62.3±21.1 years, 64.6% were men, and mean duration of MV support was 11.4±11.9 days. Prolonged MV was required in 31.3%. Requiring intubation after admission to the ICU, heart rate greater than 110, blood urea nitrogen more than 25 mg/dL, serum pH less than 7.25, serum creatinine more than 2.0 mg/dL, and a HCO3 less than 20 mEq/L were the only variables independently associated with PMV. Specificity for predicting PMV was 100% with 4 or more of these variables. CONCLUSION: The novel predictive model, using Intubation in the ICU, Tachycardia, Renal dysfunction, Acidemia, elevated Creatinine, and a decreased HCO3, was highly specific in identifying patients who subsequently required PMV support and performed better than Acute Physiology Age Chronic Health Evaluation III.

Características generales y factores predictores de mortalidad hospitalaria en pacientes con asistencia ventilatoria mecánica / General characteristics and predictive factors of Hospital mortality in patients with assistance mechanical ventilation

Objetivo: Estudiar características generales y factores predictores de mortalidad hospitalaria en pacientes internados en Unidad de Terapia Intensiva (UTI) que requirieron Asistencia ventilatoria mecánica (AVM).Diseño: Estudio prospectivo, observacional, 36 meses de duración. Ámbito: Terapia intensiva polivalente. Hospital universitario. Pacientes: AVM ≥24 horas. Variables de interés: Datos demográficos; tipo de patología de ingreso a UTI; tiempo de internación en UTI y hospital; escores al ingreso, PCR (mg/dl); AVM: motivo de inicio, permanencia y complicaciones asociadas; evolución. Resultados: 372 pacientes requirieron AVM. Edad media 52 años (r 18-93), 67% eran varones. Estadía en UTI fue 12,59 (±13,52) días y Hospital de 20,31 (±25,20). 53,2% fallecieron en UTI. Se realizó comparación de variables entre sobrevivientes y fallecidos. Análisis de regresión logística múltiple se incluyeron: edad, PCR, APACHE II, SAPS II, SOFA, patología médica, insuficiencia respiratoria y utilización de vasopresores. Mayor valor predictivo fueron: edad, patología médica, uso de vasopresores y PCR. (D de Sommer = 0,59). Sensibilidad: 68,15% (60,18 - 75,22), especificidad: 75,35% (68,93 - 80,84), VPP 66,88% (58,94 - 73,99), VPN 76,42% (70,01­ 81,84). La curva COR presenta un AUC de 0,793 (IC 95%: 0,747-0,839) p = 0,000.Conclusiones: Factores predictores de mortalidad fueron: edad (p=0,007), utilización de vasopresores (p=0,000) y patología médica (p=0,002). PCR, mostró un valor de p cercano al valor de significación (p=0,065).(AU)

Objective: Examining the general characteristics and predictive factors of inhospital mortality in patients in Intensive Care Unit (ICU) who needed mechanical ventilatory support (MVS). Design: Prospective and observational study, 36 months duration. Area: Multipurpose intensive therapy. University hospital. Patients: MVS≥24 hours. Variables of interest: Demographic data, type of pathology on admission to ICU, hospitalization time, scoring, C - reactive protein (CRP) (mg/dl), MVS: reason for initiation, duration and complications associated; evolution. Results: 372 of the patients required MVS, average age was 52 years old (r 18-93), 67% were men. Intensive care unit length of stay was 12, 59 (±13, 52) days and hospital length of stay was 20, 31 (±25, 20). 53,2% died in ICU. A comparison of variables between survivors and deceased was made. Multiple logistic regression (MLR) analysis included: age, PCR, APACHE II, SAPS II, SOFA, medical condition, respiratory failure and use of vasopressors. The greatest predictive value was: age, medical condition, use of vasopressors and PCR. (Sommer´s D = 0,59). Sensitivity 68,15% (60,18 - 75,22), specificity: 75,35% (68,93 - 80,84), VPP 66,88% (58,94 - 73,99), VPN 76,42% (70,01­ 81,84). AUC of ROC was 0,793 (IC 95%: 0,747-0,839) p = 0,000. Conclusion: Predictive factors of mortality were: age (p=0,007), use of vasopressors (p=0,000) and medical condition (p=0,002). CRP showed a p-value close to a significance value of (p=0,065).(AU)